CN113468601B

CN113468601B - Data privacy fusion method and device

Info

Publication number: CN113468601B
Application number: CN202110740972.4A
Authority: CN
Inventors: 李武璐; 霍昱光; 权纯
Original assignee: CCB Finetech Co Ltd
Current assignee: CCB Finetech Co Ltd
Priority date: 2021-06-30
Filing date: 2021-06-30
Publication date: 2022-08-02
Anticipated expiration: 2041-06-30
Also published as: CN113468601A

Abstract

The specification relates to the technical field of data security, and particularly discloses a data privacy fusion method and device, wherein the method comprises the following steps: the first user side processes the first characteristic data set by using a shared secret key to generate a first party privacy data set, and the first private key is used for operating the first party privacy data set to obtain a second party privacy data set and sending the second party privacy data set to the second user side; the second user side processes the second characteristic data set by using the shared secret key to generate a first second party private data set, and the second private key is used for operating the first second party private data set to obtain a second party private data set and sending the second party private data set to the first user side; the first user terminal uses the first private key to operate and disorder the second party private data set to obtain a third party private data set and sends the third party private data set to the second user terminal; the second user side uses the second private key to calculate the second party private data set to obtain a third party private data set; and the second user side fuses the third party privacy data set and the third party privacy data set.

Description

Data privacy fusion method and device

Technical Field

The present disclosure relates to the field of data security technologies, and in particular, to a data privacy fusion method and apparatus.

Background

At present, data scale and capacity are one of main competitiveness of enterprise institutions, each enterprise has the data volume of expansion self, the application requirements of data coverage and comprehensiveness are improved, third-party data service institutions specialized in data transaction are generated in succession, illegal data transaction behaviors of partial black and grey products are also promoted, business modes and industry criteria in the field are not completely established, and related regulation policies are gradually improved. Therefore, a data sharing and fusion scheme for really protecting privacy of each party (user privacy + provider privacy) needs to be explored in a technical level, so that the privacy and benefits of each party are protected to the greatest extent, and meanwhile, hidden troubles in service compliance are avoided.

For each existing policy file such as personal information security standard, when an organization shares user information (ID, mobile phone number, device number, etc.) with other organizations, authorization of a corresponding user needs to be obtained as a necessary condition. In an actual scenario, the user authorization process may have negative effects such as timeliness and user experience.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The embodiment of the specification provides a data privacy fusion method and device, and aims to solve the problem that a data privacy fusion scheme in the prior art cannot effectively protect user data privacy.

An embodiment of the present specification provides a data privacy fusion method, including: the first user side carries out first privacy processing on the first characteristic data set by using the shared secret key to generate a first-party privacy data set; the second user side carries out first privacy processing on the second characteristic data set by using the shared secret key to generate a first second-party privacy data set; the first user side carries out second privacy processing on the first party privacy data set by using a first private key to obtain a second party privacy data set, and the second party privacy data set is sent to the second user side; the second user side performs second privacy processing on the first second party privacy data set by using a second private key to obtain a second party privacy data set, and sends the second party privacy data set to the first user side; the first user side performs second privacy processing on the received second party privacy data set by using a first private key, performs random out-of-order confusion processing to obtain a third party privacy data set, and sends the third party privacy data set to the second user side; the second user side carries out second privacy processing on the received second party privacy data set by using a second private key to obtain a third party privacy data set; and the second user side performs fusion processing based on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set.

An embodiment of the present specification further provides a data privacy fusion method, which is applied to a first user side, and includes: performing first privacy processing on the first characteristic data set by using a shared secret key to generate a first-party privacy data set; carrying out second privacy processing on the first party privacy data set by using the first private key to obtain a second party privacy data set, and sending the second party privacy data set to a second user end; receiving a second party privacy data set sent by a second user side, wherein the second party privacy data set is obtained by the second user side through second privacy processing on a first party privacy data set by using a second private key, and the first party privacy data set is generated by the second user side through first privacy processing on a second characteristic data set by using a shared secret key; carrying out second privacy processing on the received second party privacy data set by using the first private key, and carrying out random out-of-order confusion processing to obtain a third party privacy data set; and sending the third party privacy data set to a second user end so that the second user end performs fusion processing on the third party privacy data set and a third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set, wherein the third party privacy data set is obtained by performing second privacy processing on the received second party privacy data set by the second user end by using a second private key.

An embodiment of the present specification further provides a data privacy fusion method, which is applied to a second user side, and includes: performing first privacy processing on the second characteristic data set by using the shared secret key to generate a first second-party privacy data set; carrying out second privacy processing on the first second-party privacy data set by using a second private key to obtain a second-party privacy data set, and sending the second-party privacy data set to the first user side; receiving a second party privacy data set sent by a first user side, wherein the second party privacy data set is obtained by the first user side through second privacy processing on a first party privacy data set by using a first private key, and the first party privacy data set is generated by the first user side through first privacy processing on a first characteristic data set by using a shared key; carrying out second privacy processing on the received second party privacy data set by using a second private key to obtain a third party privacy data set; receiving a third party privacy data set sent by the first user side, wherein the third party privacy data set is obtained by the first user side through second privacy processing on a received second party privacy data set by using a first private key; and performing fusion processing on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union set corresponding to the first feature data set and the second feature data set.

An embodiment of the present specification further provides a data privacy fusion apparatus, which is applied to a first user side, and includes: the first processing module is used for carrying out first privacy processing on the first characteristic data set by using the shared secret key so as to generate a first-party privacy data set; the second processing module is used for carrying out second privacy processing on the first party privacy data set by using the first private key to obtain a second party privacy data set and sending the second party privacy data set to a second user end; the receiving module is used for receiving a second party privacy data set sent by a second user end, wherein the second party privacy data set is obtained by the second user end through second privacy processing on a first party privacy data set by using a second private key, and the first party privacy data set is generated by the second user end through first privacy processing on a second characteristic data set by using a shared secret key; the disorder module is used for carrying out second privacy processing on the received second party privacy data set by using the first private key and carrying out random disorder confusion processing to obtain a third party privacy data set; and the sending module is used for sending the third party privacy data set to the second user end so that the second user end performs fusion processing on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set, wherein the third party privacy data set is obtained by performing second privacy processing on the received second party privacy data set by the second user end by using a second private key.

An embodiment of the present specification further provides a data privacy fusion apparatus, which is applied to a second user side, and includes: the first processing module is used for carrying out first privacy processing on the second characteristic data set by using the shared secret key so as to generate a first second-party privacy data set; the second processing module is used for carrying out second privacy processing on the first second-party privacy data set by using a second private key to obtain a second-party privacy data set and sending the second-party privacy data set to the first user side; the first receiving module is used for receiving a second party privacy data set sent by the first user side, wherein the second party privacy data set is obtained by performing second privacy processing on the first party privacy data set by the first user side by using a first private key, and the first party privacy data set is generated by performing first privacy processing on the first feature data set by using a shared key by the first user side; the third processing module is used for carrying out second privacy processing on the received second party privacy data set by using a second private key to obtain a third party privacy data set; the second receiving module is used for receiving a third party privacy data set sent by the first user side, wherein the third party privacy data set is obtained by the first user side through second privacy processing on the received second party privacy data set by using the first private key; and the fusion module is used for carrying out fusion processing on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union set corresponding to the first feature data set and the second feature data set.

Embodiments of the present specification further provide a computer device, including a processor and a memory for storing processor-executable instructions, where the processor executes the instructions to implement the steps of the data privacy fusion method in any of the above embodiments.

Embodiments of the present specification further provide a computer-readable storage medium, on which computer instructions are stored, and when executed, the instructions implement the steps of the data privacy fusion method described in any of the above embodiments.

In the embodiment of the specification, a data privacy fusion method is provided, a first user end may perform a first privacy process on a first feature data set by using a shared secret key to generate a first-party privacy data set, a second user end may input the shared secret key and a second feature data set into a random elliptic point generating function to generate a first second-party privacy data set, the first user end may perform a second privacy process on the first-party privacy data set by using a first private key to obtain a second-party privacy data set, and send the second-party privacy data set to the second user end, the second user end may perform a second privacy process on the first second-party privacy data set by using a second private key to obtain a second-party privacy data set, and send the second-party privacy data set to the first user end, the first user end may perform a second privacy process on the received second-party privacy data set by using the first private key, carrying out random out-of-order confusion processing to obtain a third party privacy data set, and sending the third party privacy data set to a second user end; the second user side carries out second privacy processing on the received second party privacy data set by using a second private key to obtain a third party privacy data set; and the second user side performs fusion processing based on the third party privacy data set and the third party privacy data set, so that a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set can be obtained. In the above scheme, since the first user side and the second user side perform privacy processing on respective feature data sets and then send the feature data sets to the other side, the privacy of the respective feature data sets can be effectively protected, and since the third party privacy data set and the third party random data set both perform the first privacy processing based on the shared key and the second privacy processing based on the first private key and the second private key, matching can be performed, data aggregation can be performed, and intersection and union can be obtained. In addition, because the third party privacy data set is subjected to random out-of-order confusion processing, the first user side and the second user side cannot know which of the feature data of the two parties are overlapped, namely the intersection content of the feature data of the two parties is not known, so that the security of the feature data can be further improved, higher-level privacy is realized, a technical basis is laid for stricter data management and control in the future, and a feasible security scheme is provided.

Drawings

The accompanying drawings, which are included to provide a further understanding of the specification, are incorporated in and constitute a part of this specification, and are not intended to limit the specification. In the drawings:

FIG. 1 illustrates a flow diagram of a data privacy fusion method in one embodiment of the present description;

FIG. 2 illustrates a flow diagram of a data privacy fusion method in one embodiment of the present description;

FIG. 3 illustrates a flow diagram of a data privacy fusion method in one embodiment of the present description;

FIG. 4 is a schematic diagram of a data privacy fusion apparatus in one embodiment of the present description;

FIG. 5 is a schematic diagram of a data privacy fusion apparatus in one embodiment of the present description;

FIG. 6 shows a schematic diagram of a computer device in one embodiment of the present description.

Detailed Description

The principles and spirit of the present description will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are presented merely to enable those skilled in the art to better understand and to implement the description, and are not intended to limit the scope of the description in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

As will be appreciated by one skilled in the art, embodiments of the present description may be embodied as a system, an apparatus, a method, or a computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.

The concept of data privacy fusion is that two parties respectively master sets A and B, the two parties need to acquire new set information of A ^ B and A ^ B, and the two parties cannot directly transmit an original set to perform element matching and intersection calculation based on safety and privacy requirements. Therefore, a double-blind privacy data sharing and fusion scheme which does not reveal the user privacy needs to be explored, the shared data among mechanisms is completely random and invisible (namely, the mechanisms still cannot locate, associate or acquire any user information after sharing the data), high-level privacy is realized, a technical basis is laid for a stricter data management and control policy in the future, and a feasible safety scheme is provided.

Based on this, this specification embodiment provides a data privacy fusion method. Fig. 1 shows a flowchart of a data privacy fusion method in an embodiment of the present specification. Although the present specification provides method operational steps or apparatus configurations as illustrated in the following examples or figures, more or fewer operational steps or modular units may be included in the methods or apparatus based on conventional or non-inventive efforts. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution sequence of the steps or the module structure of the apparatus is not limited to the execution sequence or the module structure described in the embodiments and shown in the drawings. When the described method or module structure is applied in an actual device or end product, the method or module structure according to the embodiments or shown in the drawings can be executed sequentially or executed in parallel (for example, in a parallel processor or multi-thread processing environment, or even in a distributed processing environment).

Specifically, as shown in fig. 1, a data privacy fusion method provided by one embodiment of the present specification may include the following steps.

Step S101, a first user side carries out first privacy processing on a first characteristic data set by using a shared secret key to generate a first-party privacy data set; and the second user side performs first privacy processing on the second characteristic data set by using the shared secret key to generate a first second-party privacy data set.

The method in this embodiment may be applied to a first user terminal and a second user terminal. The first user end and the second user end may be a desktop computer, a notebook, a mobile phone terminal, a PDA, etc., and any device that can establish a communication connection and has computing capability may be used, and the present formation of the first user end and the second user end is not limited in this application. The first client may have a first set of characteristic data stored therein. The second client may have a second set of characteristic data stored therein. For example, the feature data set may be a user feature list, and the user feature list may include privacy data such as an identity card number, a mobile phone number, a business registration number, and the like.

The first user side can perform first privacy processing on the first characteristic data set by using the shared secret key to generate a first-party privacy data set. The second user side can perform first privacy processing on the second characteristic data set by using the shared secret key to generate a first second-party privacy data set.

The shared secret key may be a shared secret key generated by the first user end and the second user end according to a secret key agreement mechanism. Key agreement, which may enable multiple parties (often two parties) to obtain a shared key through interaction without other nodes on the network being able to obtain any information about the shared key, includes DH (Diffie-Hellman) key exchange protocol based on elliptic curves. In one embodiment, the first and second clients may obtain elliptic curve related parameters. The first user terminal can generate a first private key and a first public key according to the elliptic curve related parameters, and send the first public key to the second user terminal. The second user terminal can generate a second private key and a second public key according to the elliptic curve related parameters, and send the second public key to the first user terminal. The first user terminal can generate a shared secret key according to the first private key and the second public key. The second user terminal may generate a shared secret key from the second private key and the first public key.

Step S102, the first user side carries out second privacy processing on the first party privacy data set by using a first private key to obtain a second party privacy data set, and the second party privacy data set is sent to the second user side; and the second user side performs second privacy processing on the first second-party privacy data set by using a second private key to obtain a second-party privacy data set, and sends the second-party privacy data set to the first user side.

Step S103, the first user terminal performs second privacy processing on the received second party privacy data set by using the first private key, performs random out-of-order confusion processing to obtain a third party privacy data set, and sends the third party privacy data set to the second user terminal; and the second user side performs second privacy processing on the received second party privacy data set by using a second private key to obtain a third party privacy data set.

And step S104, the second user side performs fusion processing based on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set.

The first user side can perform second privacy processing on the first party privacy data set by using the first private key to obtain a second party privacy data set, and the second party privacy data set is sent to the second user side. The second user side can perform second privacy processing on the first second-party privacy data set by using a second private key to obtain a second-party privacy data set, and sends the second-party privacy data set to the first user side.

Then, the first user side can perform second privacy processing on the received second party privacy data set by using the first private key, and perform random out-of-order confusion processing to obtain a third party privacy data set. Random out-of-order obfuscation is the process of randomly disordering the order of data in a data set. The second user side can carry out second privacy processing on the received second party privacy data set to obtain a third party privacy data set.

In one embodiment, the present solution may employ elliptic curve based cryptography. Accordingly, the first privacy process may input the shared key and the first feature data set into a random ellipsoid generation function. The second privacy processing may be an elliptic curve-based doubling operation. The random elliptic point generating function is an elliptic curve random element generating algorithm H with the similar characteristic of a hash function _p For a given input τ of arbitrary length, the output is a group of elliptic curves

Of (2) element(s)

Also has pseudo-randomness, irreversibility and collision resistance.

In one embodiment, the scheme may employ a discrete logarithm group-based cryptographic technique. Accordingly, the first privacy process may be a random discrete group element generation operation based on a hash function or a general random function. The second privacy process may be an exponential operation based on a discrete logarithm group. It will be appreciated that the present scheme may also be implemented using other discrete cryptographic groups or polynomial ring based associative cryptographic techniques.

In this way, the obtained third party privacy data set and the third party privacy data set are each subjected to a process based on a random elliptic point generating function of the shared key, a second privacy process using the first private key, and a second privacy process using the second private key. Therefore, data privacy fusion processing can be performed on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set.

In an embodiment, the second user may perform intersection calculation on the third party privacy data set and the third party privacy data set to obtain a feature data intersection corresponding to the first feature data set and the second feature data set. Then, the second user side may merge the third party privacy data set and the third party privacy data set, and remove the feature data intersection, so as to obtain a feature data union corresponding to the first feature data set and the second feature data set.

According to the method in the book loading embodiment, the first user side and the second user side transmit the feature data sets to the opposite side after performing privacy processing on the feature data sets, the privacy of the feature data sets can be effectively protected, and the third party privacy data set and the third party random data set are both subjected to first privacy processing based on the shared secret key and second privacy processing based on the first private key and the second private key, so that matching can be performed, data aggregation can be performed, and intersection and union can be obtained. In addition, because the third party privacy data set is subjected to random out-of-order confusion processing, the first user side and the second user side cannot know which of the feature data of the two parties are overlapped, that is, the intersection content of the feature data of the two parties is unknown, so that the security of the feature data can be further improved, higher-level privacy is realized, a technical basis is laid for stricter data control in the future, and a feasible security scheme is provided.

In some embodiments of the present description, the first client performs a first privacy process on the first feature data set by using the shared key to generate a first-party privacy data set, which may include: the first user terminal inputs the shared secret key and the first characteristic data set into a random elliptic point generating function to obtain a first elliptic point set; and the first user side conducts random out-of-order confusion on all the elliptic points in the first elliptic point set to obtain a first-party privacy data set. Specifically, after the shared secret key and the first characteristic data set are input into the random elliptic point generating function, the first user side conducts random out-of-order confusion on the obtained elliptic points to obtain a first party privacy data set. By the above mode, the privacy of the feature data can be further improved.

In some embodiments of the present specification, the first privacy processing, performed by the second user end on the second feature data set by using the shared key, to generate a first second-party privacy data set may include: the second user end inputs the shared secret key and the second characteristic data set into a random elliptic point generating function to obtain a second elliptic point set; and the second user terminal conducts random out-of-order confusion on all the elliptic points in the second elliptic point set to obtain a first party B private data set. Specifically, after the shared secret key and the second feature data set are input into the random elliptic point generating function, the second user side performs random out-of-order confusion on the obtained elliptic points to obtain the first second-party privacy data set. By the above mode, the privacy of the feature data can be further improved.

In some embodiments of this specification, the performing, by the second user, fusion processing based on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set may include: and the second user end calculates the intersection of the third party privacy data set and the third party privacy data set to obtain the feature data intersection corresponding to the first feature data set and the second feature data set. By the method, the feature data intersection corresponding to the first feature data set and the second feature data set can be obtained.

In some embodiments of the present specification, after receiving the second party privacy data set sent by the second user, the method may further include: the first user terminal generates a first random number, second privacy processing is carried out on second party privacy data by utilizing the first random number and a first private key to obtain a first party random privacy data set, and the first party random privacy data set is sent to the second user terminal; and the second user terminal generates a second random number, performs second privacy processing on the second party privacy data by using the second random number and a second private key to obtain a party B random privacy data set, and sends the party B random privacy data set to the first user terminal.

In order to enable the first user end and the second user end to know the positions of the feature data in the feature data sets in the merging set, the first user end and the second user end can respectively generate random numbers, and perform second privacy processing on the feature data by using the random numbers. Specifically, the first user side can generate a first random number, second privacy processing is performed on second party privacy data through the first random number and a first private key, a first party random privacy data set is obtained, and the first party random privacy data set is sent to the second user side, so that the second user side can know the position of the local feature data in a centralized mode after data privacy fusion is performed on the basis of the first party random privacy data set. The second user side can generate a second random number, second privacy processing is conducted on second party privacy data through the second random number and a second private key, a party B random privacy data set is obtained, and the party B random privacy data set is sent to the first user side, so that the first user side can know the position of the local feature data in a union obtained after data privacy fusion is conducted on the basis of the party B random privacy data set. By the method, the user side can conveniently acquire the position information of the local characteristic data in the characteristic data and in the centralized manner, and subsequent application, such as information completion and the like, is facilitated.

In some embodiments of this specification, after the second user performs intersection on the third party privacy data set and the third party privacy data set to obtain the feature data intersection corresponding to the first feature data set and the second feature data set, the method may further include: the second user end calculates a difference set of the intersection of the third party privacy data set and the feature data to obtain a first difference set; solving a difference set of the intersection of the third party privacy data set and the feature data to obtain a second difference set; the second user terminal performs second privacy processing on the first difference set by using a second random number to obtain a first second party random difference set; the second user terminal sends the first second party random difference set and the second difference set to the first user terminal; the first user terminal performs second privacy processing on the first second party random difference set by using the first random number to obtain a first party second party random difference set; the first user terminal performs second privacy processing on the second difference set by using the first random number to obtain a second party random difference set, and sends the second party random difference set to the second user terminal; the first user side determines a feature data union set corresponding to a first feature data set and a second feature data set based on a party B random privacy data set and a first party A and party B random difference set; and the second user side performs second privacy processing on the second Party A random difference set by using a second random number to obtain a second Party A random difference set, and determines a feature data union set corresponding to the first feature data set and the second feature data set based on the Party A random privacy data set and the second Party A random difference set.

After obtaining the feature data intersection, the second user may perform a difference calculation on the third party private data set and the feature data intersection to obtain a first difference. The feature data in the first difference set belongs to the third privacy data set and not to the feature data intersection. The second user side can calculate a difference set of the intersection of the third party privacy data set and the feature data to obtain a second difference set. The feature data in the second difference set belongs to the third party privacy data set and not to the feature data intersection. The second user end can utilize the second random number to execute second privacy processing on the first difference set to obtain a first second party random difference set. The second user terminal may send the first second square random difference set and the second difference set to the first user terminal. The first user end can utilize the first random number to respectively carry out second privacy processing on the first second party random difference set and the second difference set to obtain a first party second party random difference set and a second party random difference set. The first user terminal may send the second meld to the second user terminal.

Then, the first user end can determine a feature data union set corresponding to the first feature data set and the second feature data set based on the second party random privacy data set and the first party second party random difference set. The second user end can utilize the second random number to carry out second privacy processing on the received second party random difference set to obtain a second party A random difference set. Then, the second user end can determine the feature data union set corresponding to the first feature data set and the second feature data set based on the first party random privacy data set and the second party first party random difference set. In this way, the first feature data set and the second feature data set can be merged, and the first user end and the second user end can obtain the position of the local feature data in the merged feature data set, so as to facilitate subsequent further processing.

In some embodiments of the present description, the determining, by the second user, a feature data union, where the first feature data set corresponds to the second feature data set, based on the first-party random privacy data set and the second-party first-party random difference set, may include: the second user side carries out second privacy processing on the first party random privacy data set by using a second random number to obtain a second party random privacy data set; and the second user side combines the random privacy data set of the first party B and the random difference set of the first party B to obtain a feature data union set corresponding to the first feature data set and the second feature data set.

Specifically, the second user side may first perform the second privacy processing on the first-party random privacy data set by using the second random number, to obtain the second-party random privacy data set. And then, because the random privacy data set of the first party B and the random difference set of the second party B are both processed by the first random number and the second random number, the first party B and the second party B can be directly merged to obtain a feature data union set corresponding to the first feature data set and the second feature data set. In the above manner, the feature data union can be obtained in the second user terminal.

In some embodiments of the present description, the determining, by the first user end, a feature data union, where the first feature data set corresponds to the second feature data set, based on the second party random privacy data set and the first party second party random difference set, may include: the first user side carries out second privacy processing on the party B random privacy data set by using the first random number to obtain a party A and a party B random privacy data set; and the first user side combines the first party A and the second party B random privacy data set with the first party A and the second party B random difference set to obtain a feature data union set corresponding to the first feature data set and the second feature data set.

Specifically, the first user side may first perform the second privacy processing on the second-party random privacy data set by using the first random number, to obtain the first-party random privacy data set. And then, the random privacy data set of the first party A and the random difference set of the first party A and the second party B are processed by the first random number and the second random number, so that the random privacy data set of the first party A and the random difference set of the first party A and the second party A can be directly merged to obtain a feature data union set corresponding to the first feature data set and the second feature data set. In the above manner, the feature data union can be obtained in the first user terminal.

Based on the same inventive concept, embodiments of the present specification further provide a data privacy fusion method, which is applied to the first user side, as described in the following embodiments. Because the principle of solving the problem of the data privacy fusion method in the embodiment is similar to that of the data privacy fusion method, specific implementation may refer to implementation of the data privacy fusion method, and repeated parts are not described again. Referring to fig. 2, a flowchart of a data privacy fusion method in an embodiment of the present specification is shown. Specifically, as shown in fig. 2, a data privacy fusion method provided by one embodiment of the present specification may include the following steps.

Step S201, perform a first privacy process on the first feature data set by using the shared key to generate a first-party privacy data set.

Step S202, carrying out second privacy processing on the first party privacy data set by using the first private key to obtain a second party privacy data set, and sending the second party privacy data set to the second user side.

Step S203, receiving a second party privacy data set sent by a second user, where the second party privacy data set is obtained by the second user performing second privacy processing on the first party privacy data set by using a second private key, and the first party privacy data set is generated by the second user performing first privacy processing on the second feature data set by using a shared key.

And step S204, carrying out second privacy processing on the received second party privacy data set by using the first private key, and carrying out random out-of-order confusion processing to obtain a third party privacy data set.

Step S205, sending the third party privacy data set to the second user end, so that the second user end performs fusion processing on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set, where the third party privacy data set is obtained by performing second privacy processing on the received second party privacy data set by using the second private key by the second user end.

In some embodiments of the present description, performing a first privacy process on a first feature data set using a shared key to generate a first party privacy data set may include: inputting the shared secret key and the first characteristic data set into a random elliptic point generating function to obtain a first elliptic point set; and carrying out random out-of-order confusion on all the elliptic points in the first elliptic point set to obtain a first-party privacy data set.

In some embodiments of the present specification, after receiving the second party privacy data set sent by the second user, the method may further include: generating a first random number; carrying out second privacy processing on second party privacy data by using the first random number and the first private key to obtain a party A random privacy data set; sending the first party random privacy data set to a second user end; and receiving a second party random privacy data set sent by the second user side, wherein the second party random privacy data set is obtained by performing second privacy processing on second party privacy data by the second user side by using a second random number and a second private key.

Based on the same inventive concept, the embodiment of the present specification further provides a data privacy fusion method, which is applied to the second user side, as described in the following embodiments. Because the principle of solving the problem of the data privacy fusion method in the embodiment is similar to that of the data privacy fusion method, specific implementation may refer to implementation of the data privacy fusion method, and repeated parts are not described again. Referring to fig. 3, a flowchart of a data privacy fusion method in an embodiment of the present specification is shown. Specifically, as shown in fig. 3, a data privacy fusion method provided by one embodiment of the present specification may include the following steps.

Step S301, perform a first privacy process on the second feature data set by using the shared key to generate a first second-party privacy data set.

Step S302, a second privacy processing is carried out on the first second-party privacy data set by using a second private key to obtain a second-party privacy data set, and the second-party privacy data set is sent to the first user side.

Step S303, receiving a second party privacy data set sent by the first user, where the second party privacy data set is obtained by performing second privacy processing on the first party privacy data set by the first user using the first private key, and the first party privacy data set is generated by performing first privacy processing on the first feature data set by the first user using the shared key.

And step S304, carrying out second privacy processing on the received second party privacy data set by using a second private key to obtain a third party privacy data set.

Step S305, receiving a third party privacy data set sent by the first user, where the third party privacy data set is obtained by the first user performing a second privacy process on the received second party privacy data set by using the first private key.

And S306, carrying out fusion processing on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set.

In some embodiments of the present description, performing a first privacy process on the second feature data set using the shared key to generate a first second-party privacy data set may include: inputting the shared secret key and the second characteristic data set into a random elliptic point generating function to obtain a second elliptic point set; and carrying out random out-of-order confusion on all the elliptic points in the second elliptic point set to obtain a first party B private data set.

In some embodiments of the present specification, after receiving the second party privacy data set sent by the first user end, the method may further include: generating a second random number; carrying out second privacy processing on second party privacy data by using a second random number and a second private key to obtain a second party random privacy data set; sending the party B random privacy data set to a first user end; and receiving a first party random privacy data set sent by the first user side, wherein the first party random privacy data set is obtained by the first user side through carrying out second privacy processing on second party privacy data by utilizing a first random number and a first private key.

In some embodiments of the present description, performing fusion processing based on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set, may include: and the second user end calculates the intersection of the third party privacy data set and the third party privacy data set to obtain the feature data intersection corresponding to the first feature data set and the second feature data set.

The above method is described below with reference to a specific example, however, it should be noted that the specific example is only for better describing the present specification and should not be construed as an undue limitation on the present specification.

In this embodiment, a cryptography based on an elliptic curve, including a random elliptic point generation algorithm and a key negotiation algorithm, may be used in combination with a set random obfuscation technique to implement double-blindness, validity, and chargeable properties of the data privacy fusion scheme.

It can be assumed that the sets grasped by both parties are a ═ a ₁ ,…,a _n And B ═ B ₁ ,…,b _m In the scheme, the two parties can acquire intersection and union information after the hash function randomization processing, that is:

{H _p (a ₁ ),…,H _p (a _n )}∩{H _p (b ₁ ),…,H _p (b _m ) And

{H _p (a ₁ ),…,H _p (a _n )}∪{H _p (b ₁ ),…,H _p (b _m )}.

the above equations are exemplary only. And the corresponding relation between the intersection and the original information of the local can not be obtained (namely, both parties do not know which elements in the original set are in the intersection of the parties), so that higher-level privacy is realized.

A detailed description of this embodiment is given below:

a pretreatment stage:

step 1.1, the two parties determine the elliptic curve through negotiation

And a random point generating function H _p (. o) and determining an elliptic curve generator

This is a necessary initialization procedure; (the multiplication operations of elliptic curves are expressed in terms of exponentiation operations).

Step 1.2, the two parties generate their own private keys sk respectively ₁ ,

Wherein sk ₁ Is the private key of Party A, sk ₂ Is the private key of the second party, and the two parties respectively calculate the own public key

And discloses.

Step 1.3, the two parties extract their own user feature lists, the description of the scheme takes the device number as an example, the actual patent does not limit the type of the features (also can be identity card number, mobile phone number, business registration number, etc.), and respective device number sets a ═ are obtained ₁ ,…,a _n And B ═ B ₁ ,…,b _m }。

Step 1.4, the two parties calculate the shared key of the task according to the key negotiation mechanism

(first square calculation)

Calculation of Party B

)。

Step 1.5, the first party calculates the privacy set of the party

Privacy collection for the second party's computational party

Wherein

And

after out-of-order obfuscation on the basis of the original setTreatment result, H _p The output of the function is a random elliptic curve point.

The first round of interaction stage:

step 2.1, for i ═ 1, …, n, Party A calculation

Get privacy set C ═ { C of first round of Party A ₁ ,…,c _n }。

Step 2.2, calculate for j 1, …, m, party b

Get privacy set D ═ { D of the first round of the second party ₁ ,…,d _m }。

And 2.3, the first round of privacy sets are sent to the opposite party by the two parties A and B, and the first round of communication is finished.

And a second round of interaction stage:

step 3.1, party a sets D ═ D for the received privacy set ₁ ,…,d _m Is calculated for all elements

Then all of

And random disorder confusion is carried out again to obtain a privacy set E ═ E of the second round of the first party ₁ ,…,e _m And E is sent to the second party.

Step 3.2, Party A generates a random number r ₁ Computing a set

Will be provided with

To the second party (note that this step does not require random obfuscation of the set, and the order of the elements of set D needs to be maintained).

Step 3.3, prescription BFor received privacy set C ═ C ₁ ,…,c _n Is directly calculated

Get the privacy set F ═ { F of the second round of the second party ₁ ,…,f _n }。

Step 3.4, party B generates a random number r ₂ Computing a set

Will be provided with

To party a (note that this step does not require random obfuscation of the set, and the order of the elements of set C needs to be maintained).

Data matching and intersection solving:

step 4.1, after receiving E, party B performs intersection operation on the sets E and F locally to obtain an intersection I ₁ E n and F, and then calculating difference sets S ₁ ＝E\I ₁ ＝{s ₁ ,…,s _α And

(S ₁ i.e. belonging to E but not to I ₁ Set of elements of S ₂ The same applies).

Step 4.2, Party B is on S ₁ Each element in (1), calculating

Get a set

Note that in this round of operation, neither party B nor party A knows which elements are contained by the intersection of the two parties, because C, D, E sets are all processed randomly out of order.

Third round of interactionStage (2):

step 5.1, Party B assembles

And S ₂ Is sent to the first party, the pair

Marked as first square, pair S ₂ Labeled as party b.

Step 5.2, Party A for Collection

And S ₂ Each element in (1), calculating

And

first Party reservation set

Then sending the set

And (5) the second party is given.

Step 5.3, Party A for Collection

Each element of (1)

Computing

Then, a first party aggregation set is calculated

Privacy set of Party A

And

the order of (a) corresponds to (b).

Step 5.4, the second party receives

To the collection

Each element in (1), calculating

Get a set

For collections

Each element of

Computing

Then calculate the second party's aggregation set

Wherein privacy set of party B

And

the order of (a) corresponds to (b).

According to the algorithm mechanism, there is X ═ Y, but the element order is different, and it is easy to see that n + α ═ m + β is the number of elements in the union of both sets, and m- α ═ n- β is the number of elements in the intersection of both sets.

In this embodiment, both parties eachCan obtain a fusion set obtained by randomizing the elements of the self set and the counterpart set (using key agreement and random elliptic point generation algorithm and other related operations), wherein each element is in the shape of

Or

(in fact, if

Belong to the intersection of both parties, there is

) Besides the local information, the two parties can only obtain the element quantity information of the intersection and the union of the two parties (only the scale of the intersection and the union can be known), but the intersection element information and the element information of the union cannot be obtained, and any original information cannot be reversely deduced through the elements of the privacy set, so that a higher privacy level (the double-blind data privacy fusion characteristic) is realized.

The union X and Y obtained after the privacy fusion is blinded although the original information is lost

Etc. (in order to obtain the data characteristics of

Or

The form of the data is presented), but the data can still be applied in the scenes of data matching, marketing pushing, charging counting and the like, so that the original data privacy of each organization can be protected, the data of each organization can be gathered for value increment and enabling, and meanwhile, a certain reference can be provided for the service form without user authorization.

In fact, the traditional data sharing requiring user authorization means that after the user authorization, the related user data is provided to a third-party organization; in the scheme, any user information in the combination and the intersection cannot be mastered among the institutions, namely, the parties do not know which users are shared out, and the privacy set does not contain any user information, so that the users needing to be found for authorization cannot be determined, and the privacy is extremely high.

The method in the above embodiment uses a key agreement technique, and the shared key K is calculated between the two organizations, so that it can be ensured that the current data sharing only serves the two organizations, and confusion caused by sharing data with other organizations is avoided; the double-blind privacy data privacy fusion technology is used, based on an elliptic curve, a random elliptic point generation algorithm and a random confusion algorithm, double-blind data set fusion is realized by means of multi-round privacy communication, original data privacy among mechanisms can be protected to the maximum extent, user information leakage is avoided, and the necessity of user authorization is avoided to a certain extent. Compared with a data sharing service mode based on plaintext transmission, the scheme supports original data randomized transmission, and does not cause the risk of user information leakage or user authorization; compared with a data sharing service mode based on the traditional privacy set intersection technology (PSI), each party participating in the scheme can not acquire intersection information (can not know which elements of which original data sets are in the intersection), can only acquire intersection and union scale information, and is a solution with higher privacy; in addition, the scheme uses the key agreement protocol, so that the result of data sharing between the two parties is not mastered by a third party, and even if relevant privacy sets are disclosed, any privacy disclosure is not caused.

Based on the same inventive concept, embodiments of the present specification further provide a data privacy fusion apparatus, which is applied to a first user side, as described in the following embodiments. Because the principle of solving the problems of the data privacy fusion device is similar to that of the data privacy fusion method, the implementation of the data privacy fusion device can be referred to the implementation of the data privacy fusion method, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Fig. 4 is a block diagram of a configuration of a data privacy fusion apparatus according to an embodiment of the present specification, and as shown in fig. 4, the data privacy fusion apparatus includes: a first processing module 401, a second processing module 402, a receiving module 403, an out-of-order module 404, and a transmitting module 405, and the configuration will be described below.

The first processing module 401 is configured to perform a first privacy processing on the first feature data set by using the shared key to generate a first-party privacy data set.

The second processing module 402 is configured to perform second privacy processing on the first-party privacy data set by using the first private key to obtain a second-party privacy data set, and send the second-party privacy data set to the second user side.

The receiving module 403 is configured to receive a second party-to-b privacy data set sent by a second user, where the second party-to-b privacy data set is obtained by performing second privacy processing on a first party-to-b privacy data set by using a second private key by the second user, and the first party-to-b privacy data set is generated by performing first privacy processing on a second feature data set by using a shared key by the second user.

The disorder module 404 is configured to perform a second privacy processing on the received second party privacy data set by using the first private key, and perform a random disorder confusion processing to obtain a third party privacy data set.

The sending module 405 is configured to send the third party privacy data set to the second user end, so that the second user end performs fusion processing on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set, where the third party privacy data set is obtained by performing second privacy processing on the received second party privacy data set by using a second private key by the second user end.

Based on the same inventive concept, embodiments of the present specification further provide a data privacy fusion apparatus, which is applied to a second user side, as described in the following embodiments. Because the principle of solving the problems of the data privacy fusion device is similar to that of the data privacy fusion method, the implementation of the data privacy fusion device can be referred to the implementation of the data privacy fusion method, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Fig. 5 is a block diagram of a configuration of a data privacy fusion apparatus according to an embodiment of the present specification, and as shown in fig. 5, the data privacy fusion apparatus includes: the first processing module 501, the second processing module 502, the first receiving module 503, the third processing module 504, the second receiving module 505, and the merging module 506 describe the configuration below.

The first processing module 501 is configured to perform a first privacy processing on the second feature data set by using the shared key to generate a first second-party privacy data set.

The second processing module 502 is configured to perform second privacy processing on the first second-party privacy data set by using a second private key to obtain a second-party privacy data set, and send the second-party privacy data set to the first user side.

The first receiving module 503 is configured to receive a second party privacy data set sent by the first user, where the second party privacy data set is obtained by performing second privacy processing on the first party privacy data set by using a first private key by the first user, and the first party privacy data set is generated by performing first privacy processing on the first feature data set by using a shared key by the first user.

The third processing module 504 is configured to perform second privacy processing on the received second party privacy data set by using the second private key, so as to obtain a third party privacy data set.

The second receiving module 505 is configured to receive a third party privacy data set sent by the first user, where the third party privacy data set is obtained by performing a second privacy processing on the received second party privacy data set by using the first private key by the first user.

The fusion module 506 is configured to perform fusion processing on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set.

From the above description, it can be seen that the embodiments of the present specification achieve the following technical effects: because the first user side and the second user side transmit the respective feature data sets to the opposite side after carrying out privacy processing on the respective feature data sets, the privacy of the respective feature data sets can be effectively protected, and because the third party privacy data set and the third party random data set both carry out first privacy processing based on a shared secret key and second privacy processing based on a first private key and a second private key, the matching can be carried out, the data aggregation is carried out, and an intersection and an union are obtained. In addition, because the third party privacy data set is subjected to random out-of-order confusion processing, the first user side and the second user side cannot know which of the feature data of the two parties are overlapped, namely the intersection content of the feature data of the two parties is not known, so that the security of the feature data can be further improved, higher-level privacy is realized, a technical basis is laid for stricter data management and control in the future, and a feasible security scheme is provided.

The embodiment of the present specification further provides a computer device, which may specifically refer to a schematic structural diagram of a computer device based on the data privacy fusion method provided in the embodiment of the present specification, shown in fig. 6, where the computer device may specifically include an input device 61, a processor 62, and a memory 63. Wherein the memory 63 is for storing processor executable instructions. The processor 62, when executing the instructions, implements the steps of the data privacy fusion method described in any of the embodiments above.

In this embodiment, the input device may be one of the main apparatuses for information exchange between a user and a computer system. The input device may include a keyboard, a mouse, a camera, a scanner, a light pen, a handwriting input board, a voice input device, etc.; the input device is used to input raw data and a program for processing the data into the computer. The input device can also acquire and receive data transmitted by other modules, units and devices. The processor may be implemented in any suitable way. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The memory may in particular be a memory device used in modern information technology for storing information. The memory may include multiple levels, and in a digital system, the memory may be any memory as long as it can store binary data; in an integrated circuit, a circuit without a physical form and with a storage function is also called a memory, such as a RAM, a FIFO and the like; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card and the like.

In this embodiment, the functions and effects of the specific implementation of the computer device can be explained in comparison with other embodiments, and are not described herein again.

The present specification also provides a computer storage medium based on the data privacy fusion method, where the computer storage medium stores computer program instructions, and when the computer program instructions are executed, the computer storage medium implements the steps of the data privacy fusion method in any of the above embodiments.

In this embodiment, the storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.

In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer storage medium can be explained by comparing with other embodiments, and are not described herein again.

It will be apparent to those skilled in the art that the modules or steps of the embodiments of the present specification described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed over a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different from that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, embodiments of the present description are not limited to any specific combination of hardware and software.

It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and many applications other than the examples provided will be apparent to those of skill in the art upon reading the above description. The scope of the description should, therefore, be determined not with reference to the above description, but instead should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

The above description is only a preferred embodiment of the present disclosure, and is not intended to limit the present disclosure, and it will be apparent to those skilled in the art that various modifications and variations can be made in the embodiment of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present specification shall be included in the protection scope of the present specification.

Claims

1. A data privacy fusion method is characterized by comprising the following steps:

the first user side carries out first privacy processing on the first characteristic data set by using the shared secret key to generate a first-party privacy data set; the second user side carries out first privacy processing on the second characteristic data set by using the shared secret key to generate a first second-party privacy data set;

the first user side carries out second privacy processing on the first party privacy data set by using a first private key to obtain a second party privacy data set, and the second party privacy data set is sent to a second user side; the second user side carries out second privacy processing on the first second-party privacy data set by using a second private key to obtain a second-party privacy data set, and sends the second-party privacy data set to the first user side;

the first user side performs second privacy processing on the received second party privacy data set by using the first private key, performs random out-of-order confusion processing to obtain a third party privacy data set, and sends the third party privacy data set to the second user side; the second user side carries out second privacy processing on the received second party privacy data set by using the second private key to obtain a third party privacy data set;

and the second user side performs fusion processing based on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set.

2. A data privacy fusion method is applied to a first user end and comprises the following steps:

performing first privacy processing on the first characteristic data set by using a shared secret key to generate a first-party privacy data set;

carrying out second privacy processing on the first party privacy data set by using a first private key to obtain a second party privacy data set, and sending the second party privacy data set to a second user end;

receiving a second party privacy data set sent by the second user side, wherein the second party privacy data set is obtained by the second user side through second privacy processing on a first party privacy data set by using a second private key, and the first party privacy data set is generated by the second user side through first privacy processing on a second characteristic data set by using the shared secret key;

carrying out second privacy processing on the received second party privacy data set by using the first private key, and carrying out random out-of-order confusion processing to obtain a third party privacy data set;

and sending the third party privacy data set to the second user side, so that the second user side performs fusion processing on the third party privacy data set and a third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set, wherein the third party privacy data set is obtained by performing second privacy processing on the received second party privacy data set by using the second private key by the second user side.

3. The method of claim 2, wherein performing a first privacy process on the first feature data set using the shared key to generate a first party privacy data set comprises:

inputting the shared secret key and the first characteristic data set into a random elliptic point generating function to obtain a first elliptic point set;

and randomly disordering and confusing all the elliptic points in the first elliptic point set to obtain a first-party privacy data set.

4. The method of claim 2, further comprising, after receiving the second set of party privacy data sent by the second user:

generating a first random number;

performing second privacy processing on the second party privacy data by using the first random number and the first private key to obtain a party A random privacy data set;

sending the first party random privacy data set to the second user end;

and receiving a second party random privacy data set sent by the second user side, wherein the second party random privacy data set is obtained by performing second privacy processing on the second party privacy data by the second user side by using a second random number and the second private key.

5. A data privacy fusion method is applied to a second user end, and comprises the following steps:

performing first privacy processing on the second characteristic data set by using the shared secret key to generate a first second-party privacy data set;

carrying out second privacy processing on the first second-party privacy data set by using a second private key to obtain a second-party privacy data set, and sending the second-party privacy data set to the first user side;

receiving a second party privacy data set sent by the first user side, wherein the second party privacy data set is obtained by performing second privacy processing on a first party privacy data set by the first user side by using a first private key, and the first party privacy data set is generated by performing first privacy processing on a first feature data set by the first user side by using the shared key;

carrying out second privacy processing on the received second party privacy data set by using the second private key to obtain a third party privacy data set;

receiving a third party privacy data set sent by the first user side, wherein the third party privacy data set is obtained by the first user side through second privacy processing on the received second party privacy data set by using the first private key;

and performing fusion processing on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set.

6. The method of claim 5, wherein performing a first privacy process on the second feature data set using the shared key to generate a first second-party privacy data set comprises:

inputting the shared secret key and the second characteristic data set into a random elliptic point generating function to obtain a second elliptic point set;

and randomly disordering and confusing all the elliptic points in the second elliptic point set to obtain a first party B private data set.

7. The method of claim 5, further comprising, after receiving the second party privacy data set sent by the first user terminal:

generating a second random number;

performing second privacy processing on the second party privacy data by using the second random number and the second private key to obtain a party B random privacy data set;

sending the party B random privacy data set to the first user terminal;

and receiving a first party random privacy data set sent by the first user side, wherein the first party random privacy data set is obtained by the first user side through second privacy processing on the second party privacy data by using a first random number and the first private key.

8. The method according to claim 7, wherein performing fusion processing based on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set, includes:

and the second user end calculates the intersection of the third party privacy data set and the third party privacy data set to obtain the feature data intersection corresponding to the first feature data set and the second feature data set.

9. The method according to claim 8, further comprising, after the second user intersecting the third and third party privacy data sets to obtain an intersection of feature data corresponding to the first and second feature data sets, the second user further comprising:

the second user end calculates a difference set of the third party privacy data set and the feature data intersection to obtain a first difference set; solving a difference set of the intersection of the third party privacy data set and the feature data to obtain a second difference set;

the second user terminal performs second privacy processing on the first difference set by using the second random number to obtain a first second-party random difference set; the second user end sends the first second-party random difference set and the second difference set to the first user end;

the first user terminal utilizes the first random number to execute second privacy processing on the first party B random difference set to obtain a first party A and party B random difference set; the first user terminal performs second privacy processing on the second difference set by using the first random number to obtain a second party random difference set, and sends the second party random difference set to the second user terminal;

the first user side determines a feature data union set corresponding to the first feature data set and the second feature data set based on the party B random privacy data set and the first party A and party B random difference set;

and the second user side performs second privacy processing on the second Party A random difference set by using the second random number to obtain a second Party A random difference set, and determines a feature data union set corresponding to the first feature data set and the second feature data set based on the Party A random privacy data set and the second Party A random difference set.

10. The method of claim 9, wherein the second user determining the union of the first feature data set and the second feature data set based on the first party random privacy data set and the second party random difference set comprises:

the second user side carries out second privacy processing on the first party random privacy data set by using the second random number to obtain a second party random privacy data set;

and the second user side combines the second party A random privacy data set and the second party A random difference set to obtain a feature data union set corresponding to the first feature data set and the second feature data set.

11. The method of claim 9, wherein determining, by the first user, a union of feature data corresponding to the first feature data set and the second feature data set based on the second party random privacy data set and the first party second party random difference set comprises:

the first user side carries out second privacy processing on the party B random privacy data set by using the first random number to obtain a party A and a party B random privacy data set;

and the first user side combines the first party A and second party B random privacy data set and the first party A and second party B random difference set to obtain a feature data union set corresponding to the first feature data set and the second feature data set.

12. A data privacy fusion device is applied to a first user end and comprises:

the first processing module is used for carrying out first privacy processing on the first characteristic data set by using the shared secret key so as to generate a first-party privacy data set;

the second processing module is used for carrying out second privacy processing on the first party privacy data set by using a first private key to obtain a second party privacy data set and sending the second party privacy data set to a second user side;

a receiving module, configured to receive a second party privacy data set sent by the second user, where the second party privacy data set is obtained by performing second privacy processing on a first party privacy data set by using a second private key by the second user, and the first party privacy data set is generated by performing first privacy processing on a second feature data set by using the shared key by the second user;

the disorder module is used for carrying out second privacy processing on the received second party privacy data set by using the first private key and carrying out random disorder confusion processing to obtain a third party privacy data set;

a sending module, configured to send the third party privacy data set to the second user side, so that the second user side performs fusion processing on the third party privacy data set and a third party privacy data set to obtain a feature data intersection and a feature data union corresponding to the first feature data set and the second feature data set, where the third party privacy data set is obtained by performing second privacy processing on the received second party privacy data set by using the second private key by the second user side.

13. A data privacy fusion device is applied to a second user end, and comprises:

the first processing module is used for carrying out first privacy processing on the second characteristic data set by using the shared secret key so as to generate a first second-party privacy data set;

the second processing module is used for carrying out second privacy processing on the first second-party privacy data set by using a second private key to obtain a second-party privacy data set and sending the second-party privacy data set to the first user side;

a first receiving module, configured to receive a second party privacy data set sent by the first user, where the second party privacy data set is obtained by performing second privacy processing on a first party privacy data set by using a first private key by the first user, and the first party privacy data set is generated by performing first privacy processing on a first feature data set by using the shared key by the first user;

the third processing module is used for carrying out second privacy processing on the received second party privacy data set by using the second private key to obtain a third party privacy data set;

a second receiving module, configured to receive a third party privacy data set sent by the first user, where the third party privacy data set is obtained by performing second privacy processing on the received second party privacy data set by using the first private key by the first user;

and the fusion module is used for carrying out fusion processing on the third party privacy data set and the third party privacy data set to obtain a feature data intersection and a feature data union set corresponding to the first feature data set and the second feature data set.

14. A computer device comprising a processor and a memory for storing processor-executable instructions which, when executed by the processor, implement the steps of the method of any one of claims 2 to 11.

15. A computer-readable storage medium having computer instructions stored thereon which, when executed, implement the steps of the method of any one of claims 2 to 11.